from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-11 14:07:18.985520
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Fri, 11, Dec, 2020
Time: 14:07:22
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.5942
Nobs: 137.000 HQIC: -44.7329
Log likelihood: 1458.05 FPE: 1.71877e-20
AIC: -45.5124 Det(Omega_mle): 9.11688e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.474481 0.173765 2.731 0.006
L1.Burgenland 0.145755 0.084662 1.722 0.085
L1.Kärnten -0.295958 0.071557 -4.136 0.000
L1.Niederösterreich 0.112416 0.204165 0.551 0.582
L1.Oberösterreich 0.291202 0.169987 1.713 0.087
L1.Salzburg 0.168384 0.086235 1.953 0.051
L1.Steiermark 0.092247 0.121470 0.759 0.448
L1.Tirol 0.160109 0.080803 1.981 0.048
L1.Vorarlberg 0.004897 0.078190 0.063 0.950
L1.Wien -0.132617 0.162740 -0.815 0.415
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.519953 0.220616 2.357 0.018
L1.Burgenland 0.006503 0.107489 0.060 0.952
L1.Kärnten 0.337772 0.090851 3.718 0.000
L1.Niederösterreich 0.128854 0.259214 0.497 0.619
L1.Oberösterreich -0.194928 0.215820 -0.903 0.366
L1.Salzburg 0.195335 0.109486 1.784 0.074
L1.Steiermark 0.227976 0.154222 1.478 0.139
L1.Tirol 0.147940 0.102589 1.442 0.149
L1.Vorarlberg 0.203066 0.099273 2.046 0.041
L1.Wien -0.554381 0.206619 -2.683 0.007
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.311446 0.075399 4.131 0.000
L1.Burgenland 0.106353 0.036736 2.895 0.004
L1.Kärnten -0.018654 0.031050 -0.601 0.548
L1.Niederösterreich 0.125402 0.088590 1.416 0.157
L1.Oberösterreich 0.276884 0.073760 3.754 0.000
L1.Salzburg -0.009776 0.037419 -0.261 0.794
L1.Steiermark -0.041973 0.052708 -0.796 0.426
L1.Tirol 0.089848 0.035061 2.563 0.010
L1.Vorarlberg 0.131366 0.033928 3.872 0.000
L1.Wien 0.036790 0.070615 0.521 0.602
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.182659 0.087588 2.085 0.037
L1.Burgenland 0.000195 0.042675 0.005 0.996
L1.Kärnten 0.031220 0.036069 0.866 0.387
L1.Niederösterreich 0.052183 0.102912 0.507 0.612
L1.Oberösterreich 0.377569 0.085684 4.407 0.000
L1.Salzburg 0.089890 0.043468 2.068 0.039
L1.Steiermark 0.202856 0.061228 3.313 0.001
L1.Tirol 0.034644 0.040729 0.851 0.395
L1.Vorarlberg 0.108115 0.039413 2.743 0.006
L1.Wien -0.081639 0.082031 -0.995 0.320
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.627486 0.188534 3.328 0.001
L1.Burgenland 0.077119 0.091858 0.840 0.401
L1.Kärnten -0.007184 0.077639 -0.093 0.926
L1.Niederösterreich -0.069196 0.221518 -0.312 0.755
L1.Oberösterreich 0.110447 0.184435 0.599 0.549
L1.Salzburg 0.039293 0.093564 0.420 0.675
L1.Steiermark 0.125304 0.131795 0.951 0.342
L1.Tirol 0.233534 0.087670 2.664 0.008
L1.Vorarlberg 0.028687 0.084836 0.338 0.735
L1.Wien -0.147346 0.176572 -0.834 0.404
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.189361 0.129942 1.457 0.145
L1.Burgenland -0.041279 0.063311 -0.652 0.514
L1.Kärnten -0.010272 0.053511 -0.192 0.848
L1.Niederösterreich 0.183102 0.152676 1.199 0.230
L1.Oberösterreich 0.385915 0.127117 3.036 0.002
L1.Salzburg -0.030515 0.064487 -0.473 0.636
L1.Steiermark -0.038374 0.090836 -0.422 0.673
L1.Tirol 0.194285 0.060425 3.215 0.001
L1.Vorarlberg 0.039345 0.058471 0.673 0.501
L1.Wien 0.138641 0.121698 1.139 0.255
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.219526 0.165023 1.330 0.183
L1.Burgenland 0.071916 0.080403 0.894 0.371
L1.Kärnten -0.072177 0.067957 -1.062 0.288
L1.Niederösterreich -0.071232 0.193894 -0.367 0.713
L1.Oberösterreich -0.092584 0.161435 -0.574 0.566
L1.Salzburg 0.009611 0.081896 0.117 0.907
L1.Steiermark 0.386246 0.115359 3.348 0.001
L1.Tirol 0.526853 0.076737 6.866 0.000
L1.Vorarlberg 0.225836 0.074257 3.041 0.002
L1.Wien -0.199834 0.154553 -1.293 0.196
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.118228 0.190924 0.619 0.536
L1.Burgenland 0.027927 0.093022 0.300 0.764
L1.Kärnten -0.087590 0.078623 -1.114 0.265
L1.Niederösterreich 0.165105 0.224327 0.736 0.462
L1.Oberösterreich 0.038741 0.186773 0.207 0.836
L1.Salzburg 0.218400 0.094751 2.305 0.021
L1.Steiermark 0.167481 0.133466 1.255 0.210
L1.Tirol 0.067712 0.088782 0.763 0.446
L1.Vorarlberg 0.028359 0.085912 0.330 0.741
L1.Wien 0.268379 0.178811 1.501 0.133
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.597830 0.105291 5.678 0.000
L1.Burgenland -0.012253 0.051300 -0.239 0.811
L1.Kärnten 0.001243 0.043359 0.029 0.977
L1.Niederösterreich -0.039779 0.123712 -0.322 0.748
L1.Oberösterreich 0.286603 0.103002 2.782 0.005
L1.Salzburg 0.006701 0.052253 0.128 0.898
L1.Steiermark 0.017946 0.073604 0.244 0.807
L1.Tirol 0.071354 0.048962 1.457 0.145
L1.Vorarlberg 0.177548 0.047379 3.747 0.000
L1.Wien -0.099839 0.098611 -1.012 0.311
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.107595 -0.025545 0.189895 0.245397 0.031869 0.078526 -0.133551 0.133502
Kärnten 0.107595 1.000000 -0.053121 0.183132 0.109963 -0.152896 0.184348 0.012901 0.267938
Niederösterreich -0.025545 -0.053121 1.000000 0.245834 0.059206 0.186777 0.087719 0.029399 0.365671
Oberösterreich 0.189895 0.183132 0.245834 1.000000 0.255834 0.266540 0.078042 0.061680 0.057030
Salzburg 0.245397 0.109963 0.059206 0.255834 1.000000 0.136202 0.045912 0.081330 -0.047739
Steiermark 0.031869 -0.152896 0.186777 0.266540 0.136202 1.000000 0.083272 0.065110 -0.170235
Tirol 0.078526 0.184348 0.087719 0.078042 0.045912 0.083272 1.000000 0.130443 0.109170
Vorarlberg -0.133551 0.012901 0.029399 0.061680 0.081330 0.065110 0.130443 1.000000 0.060999
Wien 0.133502 0.267938 0.365671 0.057030 -0.047739 -0.170235 0.109170 0.060999 1.000000